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| from torch.utils.data import Dataset, DataLoader | |
| from pathlib import Path | |
| from typing import List, Tuple, TypeVar, Optional | |
| import pandas as pd | |
| import os | |
| from PIL import Image | |
| from torch import Tensor | |
| from torchvision import transforms | |
| import torch | |
| PathLike = TypeVar("PathLike", str, Path, None) | |
| PROJECT_DIR = Path(os.path.realpath(__file__)).parent.parent | |
| DATADIR = Path("<PATH_TO_DATA>/JSRT") | |
| # can be found at http://db.jsrt.or.jp/eng.php | |
| def build_dataloaders( | |
| data_dir: str=DATADIR, | |
| img_size: int=128, | |
| batch_size: int=16, | |
| num_workers: int=1, | |
| n_labelled_images: Optional[int] = None, | |
| **kwargs | |
| ) -> Tuple[List, List, List]: | |
| """ | |
| Build dataloaders for the JSRT dataset. | |
| """ | |
| train_ds = JSRTDataset(data_dir, PROJECT_DIR / "data", "JSRT_train_split.csv", img_size) | |
| if n_labelled_images is not None: | |
| train_ds = torch.utils.data.Subset(train_ds, range(n_labelled_images)) | |
| print(f"Using {n_labelled_images} labelled images") | |
| val_ds = JSRTDataset(data_dir, PROJECT_DIR / "data", "JSRT_val_split.csv", img_size) | |
| test_ds = JSRTDataset(data_dir, PROJECT_DIR / "data", "JSRT_test_split.csv", img_size) | |
| dataloaders = {} | |
| dataloaders['train'] = DataLoader(train_ds, batch_size=batch_size, | |
| shuffle=True, num_workers=num_workers, | |
| pin_memory=True) | |
| dataloaders['val'] = DataLoader(val_ds, batch_size=batch_size, | |
| shuffle=False, num_workers=num_workers, | |
| pin_memory=True) | |
| dataloaders['test'] = DataLoader(test_ds, batch_size=batch_size, | |
| shuffle=False, num_workers=num_workers, | |
| pin_memory=True) | |
| return dataloaders | |
| class JSRTDataset(Dataset): | |
| def __init__(self, base_path:PathLike, | |
| csv_path:PathLike, | |
| csv_name:str, | |
| img_size:int=128, | |
| labels:List[str] =('right lung', 'left lung', ), | |
| **kwargs) -> None: | |
| self.df = pd.read_csv(os.path.join(csv_path, csv_name)) | |
| self.base_path = Path(base_path) | |
| self.labels = labels | |
| self.img_size = img_size | |
| def load_image(self, fname: str) -> Tensor: | |
| img = Image.open(self.base_path /fname).convert('L').resize((self.img_size, self.img_size)) | |
| img = transforms.ToTensor()(img).float() | |
| return img | |
| def load_labels(self, fnames: List[str]) -> Tensor: | |
| labels = [] | |
| for fname in fnames: | |
| label = Image.open(self.base_path /fname).convert('L').resize((self.img_size, self.img_size)) | |
| # convert to tensor | |
| label = transforms.ToTensor()(label).float() | |
| # make binary | |
| label = (label > .5).float() | |
| labels.append(label) | |
| # append all labels and merge | |
| label = torch.stack(labels).sum(0) | |
| # lungs have no overlap (right?) | |
| if (label > 1).sum()>0: | |
| print("overlapping lungs!", fnames) | |
| label = (label > .5) | |
| return label | |
| def __getitem__(self, index) -> Tuple[Tensor, Tensor]: | |
| i = self.df.index[index] | |
| img = self.load_image(self.df.loc[i, "path"]) | |
| label_paths = ["SCR/masks/" + item + "/" + self.df.loc[i, 'id']+ ".gif" for item in self.labels] | |
| labels = self.load_labels(label_paths) | |
| return img, labels | |
| def __len__(self): | |
| return len(self.df) | |